3D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Clouds

Understand

The manual annotation for large-scale point clouds is still tedious and unavailable for many harsh real-world tasks.

• Self-supervised learning, which is used on raw and unlabeled data to pre-train deep neural networks, is a promising approach to address this issue.
• Existing works usually take the common aid from auto-encoders to establish the self-supervision by the self-reconstruction schema.
• However, the previous auto-encoders merely focus on the global shapes and do not distinguish the local and global geometric features apart.